Intrusion detection ensemble algorithm based on neighborhood rough set
Wei Ju · Computer Engineering and Applications Journal · 2014
The intrusion detection data has high dimensionality and nonlinear characteristics, and contains large redundant and noisy attributes, as well as some continuous attributes, this paper presents an ensemble algorithm based on neighborhood rough set to improve the effect of intrusion detection. Many training subsets are generated by Bagging technology, reduced training subsets with large difference are gained using neighborhood rough set with different radius in the training subset,many base classifiers are trained in reduced training subsets, and are ensembled using weighted average method. The experimental results in the KDD99 dataset show that the algorithm can effectively improve the accuracy and efficiency of intrusion detection, it has high generalization and stability.